Welcome to the InfoSci Platform
Reference Hub4
A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series

A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series

Pengcheng Zhang, Yan Xiao, Yuelong Zhu, Jun Feng, Dingsheng Wan, Wenrui Li, Hareton Leung
Copyright: © 2016 |Volume: 13 |Issue: 3 |Pages: 20
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781466689060|DOI: 10.4018/IJWSR.2016070102
Cite Article Cite Article

MLA

Zhang, Pengcheng, et al. "A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series." IJWSR vol.13, no.3 2016: pp.26-45. http://doi.org/10.4018/IJWSR.2016070102

APA

Zhang, P., Xiao, Y., Zhu, Y., Feng, J., Wan, D., Li, W., & Leung, H. (2016). A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series. International Journal of Web Services Research (IJWSR), 13(3), 26-45. http://doi.org/10.4018/IJWSR.2016070102

Chicago

Zhang, Pengcheng, et al. "A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series," International Journal of Web Services Research (IJWSR) 13, no.3: 26-45. http://doi.org/10.4018/IJWSR.2016070102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Most of the time series data mining tasks attempt to discover data patterns that appear frequently. Abnormal data is often ignored as noise. There are some data mining techniques based on time series to extract anomaly. However, most of these techniques cannot suit big unstable data existing in various fields. Their key problems are high fitting error after dimension reduction and low accuracy of mining results. This paper studies an approach of mining time series abnormal patterns in the hydrological field. The authors propose a new idea to solve the problem of hydrological anomaly mining based on time series. They propose Feature Points Symbolic Aggregate Approximation (FP_SAX) to improve the selection of feature points, and then measures the distance of strings by Symbol Distance based Dynamic Time Warping (SD_DTW). Finally, the distances generated are sorted. A set of dedicated experiments are performed to validate the authors' approach. The results show that their approach has lower fitting error and higher accuracy compared to other approaches.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.